Bayesian Tobit quantile regression with single-index models

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

20 Scopus Citations
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Author(s)

Detail(s)

Original languageEnglish
Pages (from-to)1247-1263
Journal / PublicationJournal of Statistical Computation and Simulation
Volume85
Issue number6
Publication statusPublished - 13 Apr 2015
Externally publishedYes

Abstract

Based on the Bayesian framework of utilizing a Gaussian prior for the univariate nonparametric link function and an asymmetric Laplace distribution (ALD) for the residuals, we develop a Bayesian treatment for the Tobit quantile single-index regression model (TQSIM). With the location-scale mixture representation of the ALD, the posterior inferences of the latent variables and other parameters are achieved via the Markov Chain Monte Carlo computation method. TQSIM broadens the scope of applicability of the Tobit models by accommodating nonlinearity in the data. The proposed method is illustrated by two simulation examples and a labour supply dataset.

Research Area(s)

  • Bayesian quantile regression, Gaussian process prior, Markov chain Monte Carlo methods, Tobit single-index models